Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT

Citation Author(s):
Zhi-Hao
Chen
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Submitted by:
Zhihao Chen
Last updated:
Tue, 07/28/2020 - 11:50
DOI:
10.21227/4kcm-m312
Data Format:
License:
3.5
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Abstract 

This paper applies AI (artificial intelligence) technology to analyze low-dose HRCT (High-resolution chest radiography) data in an attempt to detect COVID-19 pneumonia symptoms. A new model structure is proposed with segmentation of anatomical structures on DNNs-based (deep learning neural network) methods, relying on an abundance of labeled data for proper training. The model improves the existing techniques used for low-dose HRCT image inspection through an application of Stacked Autoencoders (SAEs) structures using the segmentation function for the area object detection model on Mask-RCNN. As a result, the proposed approach can quickly analyze X-ray images in detecting abnormalities in patients with lab-confirmed coronavirus even before clinical symptoms appear. In addition to detecting early abnormalities, area object detection model reveals a finding not seen in the latest cases of COVID-19. Most noteworthy, the study has shown that all COVID-19 patients exhibit an associated bilateral pleural effusion. The features are augmented to the model for the improvement of detection quality improvement and the shorten of the examination period.

Instructions: 

This tool model propose a Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT architecture. Based on autoencoder of Mask-RCNN for area mark feature maps objection detection for the identification of COVID-19 pneumonia have very serious pathological and always accompanied by various of symptoms. We collect a lot of lung x-ray images were be integrated into DICM style dataset prepare for experiment on computer on vision algorithms, and deep learning architecture based on autoencoder of Mask- RCNN algorithms are the main technological breakthrough.

Comments

The images showing in the article are CT images. However, the authors discussed X-ray images for COVID-19 detection. Please elaborate on it.

Submitted by Debanjan Konar on Sun, 08/02/2020 - 10:18